Search Results (5,534)

During a widespread epidemic, a large portion of the population faces an increased risk of contracting infectious diseases such as COVID-19, monkeypox, and pneumonia. These outbreaks often trigger cascading effects, significantly impacting society and healthcare systems. To contain the spread, the Centers for Disease Control and Prevention (CDC) must monitor infected individuals (targets) and their geographical locations (areas) as a basis for allocating medical resources. This scenario is a Target-to-Area (TTA) problem. Previous research introduced the Point-In-Polygon (PIP) technique to address multi-target and single-area TTA problems. PIP technology relies on an area’s boundary points to determine whether a target is within that region. However, when dealing with multi-target, multi-area TTA problems, PIP alone may have limitations. The K-Nearest Neighbors (KNN) algorithm presents a promising alternative, but its classification accuracy depends on the availability of sufficient samples, i.e., known targets and their corresponding geographical areas. When sample data are limited, the effectiveness of KNN is constrained, potentially delaying the CDC’s ability to track and manage outbreaks. For this problem, this study proposes an improved approach that integrates PIP and KNN technologies while introducing area boundary points as additional samples. This enhancement aims to improve classification accuracy and mitigate the impact of insufficient sample data on epidemic tracking and management. Full article

Site selection is the key to carrying out the industrial layout of construction and demolition waste (CDW) resourcing enterprises. The current study needs more data on CDW industry location. The current construction waste resource utilization rate and industrial layout need to be improved. This study uses statistical and visualization methods to analyze key factors affecting the location of CDW recycling enterprises. Additionally, it identifies planning strategies and policy incentives to drive industry development. The study explicitly adopts global and weighted geographic regression (GWR) analysis methods and uses ArcGIS 10.8 to visualize point of interest (POI) data. It was found that (1) the main factors affecting the spatial distribution of the CDW recycling economy, in order of importance, are river network density, financial subsidies, R&D incentives, the number of building material markets, the value added by the secondary industry, the area of industrial land, and the density of the road network. The three main drivers of site selection decisions are government subsidies, market size, land, and transportation resources. (2) Enterprise industry chain and transportation costs are industrial economic decision-making considerations. Enterprises are generally located on flat terrain, around industrial parks, near the center of urban areas, and close to demand and cost reduction. (3) At the city level, there are more resource-based enterprises in cities with high levels of economic development and strong policy support. The spatial distribution of enterprises is consistent with the direction of urban geographic development. There is a positive global correlation between construction waste resourcing enterprises. Ningbo, western Qingdao, and northern Beijing show high aggregation characteristics. Low–low aggregation characteristics exist in regions other than central Chongqing. High–low aggregation characteristics are found in the center of the main city of Chongqing, eastern Shanghai, and central Nanjing. Low–high aggregation is distributed in northeastern Ningbo, northern Guangzhou, and southern Shenzhen. (4) Regarding industrial agglomeration, except for Nanjing, construction waste industrial agglomeration occurs in all 11 pilot cities. Among them, Shanghai, Xiamen, and Hangzhou have industries that are distributed evenly. Xi’an and Chongqing have a centralized distribution of industries. Guangzhou, Shenzhen, Beijing, Ningbo, and Qingdao have multi-center clustering of industries. Nanning’s industry has a belt-shaped distribution. This research explores the micro elements of industry chain integration in the CDW industry. It combines incentive policies and urban planning at the macro level. Together, these efforts promote sustainable city construction. This research provides CDW location data and dates for future digital twin and city model algorithms. It supports industrial planning, transportation, spatial optimization, carbon emission analysis, city operations, and management and aims to enhance the city’s green and low-carbon operations. Full article

Ecosystem services (ESs) are increasingly recognized as critical to sustainable development and human well-being and are frequently used as indicators in environmental governance policies. However, existing studies mostly assess the performance of isolated single ESs, ignoring the management data needs of local governments for comprehensive gate-keeping and the easy monitoring of regional ecosystems, and lacking holistic gate-keeping indicators for local ESs. To address these shortcomings, this study assessed the spatial changes in five main ESs in the Yangtze River basin (YTRB) in China by creating a comprehensive ESs indicator (CESI) using multi-source data, and introduced the hotspot analyses and spatial econometric models to explore the driving forces of CESI. Results showed that during the study period, the CESI in the YTRB increased from 0.44 in 2000 to 0.47 in 2020. High-value areas were mainly concentrated in the hilly and mountainous regions, whereas the low-value areas were predominantly situated in the plain areas. From 2000 to 2020, the hot spots of CESI were primarily located in the middle and the lower reaches of the YTRB. Conversely, the cold spots were situated in the upper reaches of the YTRB. The regression analysis revealed a significant negative association between socioeconomic factors and CESI, while a significant positive association between natural background factors and CESI. Of the natural background factors, average precipitation has the largest positive effect on CESI, with each 1% increase resulting in up to 0.369% increase in CESI. In contrast, GDP density had the greatest negative impact on CESI, with each 1% increase triggering a reduction in CESI of up to 6.210%. The findings suggest that CESI, which integrates multiple ESs, can effectively simplify the difficulty of regional ecological regulation. The driving mechanism indicates that environmental protection policies, when combined with the natural conditions and intensity of human activities in the region, would be more coherent with varying regulatory intensities. Full article

Contrastive learning has emerged as a dominant paradigm for understanding 3D open-world environments, particularly in the realm of multi-modalities. However, due to the nature of self-supervised learning and the limited size of 3D datasets, pre-trained models in the 3D point cloud domain often suffer from overfitting in downstream tasks, especially in zero-shot classification. To tackle this problem, we design a module to mine and enhance hard negatives from datasets, which are useful to improve the discrimination of models. This module could be seamlessly integrated into cross-modal contrastive learning frameworks, addressing the overfitting issue by enhancing the mined hard negatives during the process of training. This module consists of two key components: mining and enhancing. In the process of mining, we identify hard negative samples by examining similarity relationships between vision–vision and vision–text modalities, locating hard negative pairs within the visual domain. In the process of enhancing, we compute weighting coefficients via the similarity differences of these mined hard negatives. By enhancing the mined hard negatives while leaving others unchanged, we improve the overall performance and discrimination of models. A series of experiments demonstrate that our module can be easily incorporated into various contrastive learning frameworks, leading to improved model performance in both zero-shot and few-shot tasks. Full article

The overarching goal of this research is to evaluate the energy demands and performance of multi-storey cross-laminated timber (CLT) buildings. The research examines the various energy demands influencing the performance of multi-storey CLT buildings. The study addresses the following research question: Can different energy demands influence the performance of CLT buildings? The investigation explores building modeling and simulation under two different weather scenarios to assess these issues. The study considers London Islington and St Albans (Test Reference Year—TRY), due to the proximity of the actual case studies to the reference locations of the weather files. The investigation captures energy demands and performance in the warm season (i.e., May–August). The findings show that the Stadt building (STB) temperatures under the two weather scenarios are warmer by 1.2 °C and 1.6 °C than those of Brid building (BDH) under the same weather conditions. Outdoor dry-bulb temperatures have a lesser impact on radiant temperatures than indoor air temperatures and operative temperatures in the buildings. Solar gains for external windows are influenced by design variables (e.g., building shapes, heights, floor areas, orientations, opening sizes, etc.). The indoor environmental conditions of the buildings under different weather conditions are comfortable, except for BDH St Albans TRY. Occupancy is a major driver influencing domestic hot water (DHW) usage profiles, regardless of the energy sources in the buildings. DHW is a significant parameter determining the overall energy usage in buildings. Other energy usage profiles, such as room electricity, computers and equipment, general lighting, and lighting, can also impact energy usage in buildings. The research outcomes can enhance our understanding of energy usage profiles and possible improvements to enhance the overall performance of CLT buildings. Full article

Emotion recognition in conversation (ERC) is an important research direction in the field of human-computer interaction (HCI), which recognizes emotions by analyzing utterance signals to enhance user experience and plays an important role in several domains. However, existing research on ERC mainly focuses on constructing graph networks by directly modeling interactions on multimodal fused features, which cannot adequately capture the complex dialog dependency based on time, speaker, modalities, etc. In addition, existing multi-task learning frameworks for ERC do not systematically investigate how and where gender information is injected into the model to optimize ERC performance. To address the above problems, this paper proposes a Hierarchical Graph Fusion for ERC with Mid-Late Gender-aware Strategy (HGF-MiLaG). HGF-MiLaG uses hierarchical fusion graph to adequately capture intra-modal and inter-modal speaker dependency and temporal dependency. In addition, HGF-MiLaG explores the effect of the location of gender information injections on ERC performance, and ultimately employs a Mid-Late multilevel gender-aware strategy in order to allow the hierarchical graph network to determine the proportion of emotion and gender information in the classifier. Empirical results on two public multimodal datasets(i.e.,IEMOCAP and MELD), demonstrate that HGF-MiLaG outperforms existing methods. Full article

To create an inclusive city, it is essential to have accessible pedestrian infrastructure. The accessibility of pedestrian infrastructure is ensured through the proper maintenance of sidewalks. When resources are limited, it is necessary to prioritize sidewalks by identifying those in the most critical condition, and this is often achieved through multi-criteria analyses. This paper proposed an analysis of the criteria to be considered, which include not only pavement distresses but also the importance of the sidewalk in connecting various parts of the city and ensuring accessibility to significant places for all, including vulnerable users. Methodologies for evaluating a sidewalk in relation to these criteria were proposed and an application of these methods to a simple case study in Genoa was presented. In this context, the evaluation of the criteria weights was performed using subjective and objective methods. The weights calculated with the two methods generated the same priorities. All the experts interviewed agreed with the proposed set of criteria and two experts suggested considering a new criterion relating to the level of danger of the context in which a pavement is located. Full article

Under the combined influence of climate change, accelerated urbanization, and inadequate urban flood defense standards, urban pluvial flooding has become an increasingly severe issue. This not only poses significant challenges to social stability and economic development but also makes accurate flood risk assessment crucial for improving urban flood control and drainage capabilities. This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. By constructing a hydrological–hydrodynamic coupled model using the SWMM and LISFLOOD-FP, this study evaluates the drainage capacity of the pipe network and surface inundation characteristics under both historical and design rainfall scenarios. An agent-based model (ABM) is developed to analyze the dynamic risks and vulnerabilities of population and building agents under different rainfall scenarios, capturing macroscopic emergent patterns from individual behavior rules and analyzing them in both time and space dimensions. Additionally, using multi-source remote sensing data, dynamic population vulnerability, and flood hazard processes, a quantitative dynamic flood risk analysis is conducted based on cloud models. The results demonstrated the following: (1) PSO performed best in calibrating the SWMM in the study area, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.93 to 0.69. (2) Drainage system capacity was low, with over 90% of the network exceeding capacity in scenarios with return periods of 1 to 100 years. (3) The vulnerability of people and buildings increased with higher flood intensity and duration. Most affected individuals were located on roads. In Event 6, 11.41% of buildings were at risk after 1440 min; in the 20-year flood event, 26.69% of buildings were at risk after 180 min. (4) Key features influencing vulnerability included the DEM, PND, NDVI, and slope. High-risk areas in the study area expanded from 36.54% at 30 min to 38.05% at 180 min. Full article

Small targets with low radar cross-section (RCS) and high velocities are very hard to track by radar as long as the frequent variations in speed and location demand shorten the integration temporal window. In this paper, we propose a technique for tracking evasive targets using a continuous wave (CW) radar array of multiple transmitters operating in the millimeter wavelength (MMW). The scheme is demonstrated to detect supersonic moving objects, such as rifle projectiles, with extremely short integration times while utilizing an adaptive processing algorithm of the received signal. Operation at extremely high frequencies qualifies spatial discrimination, leading to resolution improvement over radars operating in commonly used lower frequencies. CW transmissions result in efficient average power utilization and consumption of narrow bandwidths. It is shown that although CW radars are not naturally designed to estimate distances, the array arrangement can track the instantaneous location and velocity of even supersonic targets. Since a CW radar measures the target velocity via the Doppler frequency shift, it is resistant to the detection of undesired immovable objects in multi-scattering scenarios; thus, the tracking ability is not impaired in a stationary, cluttered environment. Using the presented radar scheme is shown to enable the processing of extremely weak signals that are reflected from objects with a low RCS. In the presented approach, the significant improvement in resolution is beneficial for the reduction in the required detection time. In addition, in relation to reducing the target recording time for processing, the presented scheme stimulates the detection and tracking of objects that make frequent changes in their velocity and position. Full article

Finding optimal locations for wind farms requires a delicate balance between maximizing energy generation potential and addressing the socio-economic implications for local communities, particularly in regions facing socio-economic challenges. While existing research often focuses on technical and economic aspects of wind farm siting, this study addresses a crucial research gap by integrating sustainable supply-chain-management principles into a comprehensive site-selection framework. We present a novel approach that combines Geographic-Information-System-based spatial analysis, the Fuzzy Analytic Hierarchy Process, and multi-criteria decision-making techniques to identify and prioritize optimal wind farm locations in Tunisia. Our framework considers not only traditional factors, like wind speed, terrain slope, and road and grid infrastructure, but also crucial socio-economic indicators, such as unemployment rates, population density, skilled workforce availability, and land cost. Based on the spatial analysis, it was revealed that 33,138 km² was appropriate for deploying large-scale wind systems, of which 6912 km² (4.39% of the total available area) was categorized as “most suitable”. Considering the SSCM evaluation criteria, despite the minor variations, the ARAS, COPRAS, EDAS, MOORA, VIKOR, and WASPAS techniques showcased that Kasserine, Kebili, and Bizerte stood as ideal locations for hosting large-scale wind systems. These rankings were further validated by the Averaging, Borda, and Copeland methods. By incorporating this framework, the study identifies locations where wind energy development can be a catalyst for economic growth, social upliftment, and improved livelihoods. This holistic approach facilitates informed decision making for policymakers and investors, thus ensuring that wind energy projects contribute to a more sustainable and equitable future for all stakeholders. Full article

As the last link of power transmission, the safe operation of the distribution network directly affects the experience of power users, and short-time distribution network faults can cause huge economic losses. There are few fault recording devices in rural or suburban distribution networks, and it is difficult to upload information, which brings difficulties to accurate fault location. In order to improve the accuracy of fault location, this study proposes a fault location method for distribution networks based on electric field-coupled voltage sensing and multi-source information fusion. First, an optimized resource pool architecture is proposed, and a distribution network data fusion platform is established based on this architecture to effectively integrate voltage, current and other fault data. Second, in order to overcome the problem of expanding the fault location range that may be caused by the current-based matrix algorithm, this study proposes an improved directed graph-based matrix algorithm and combines it with the matrix algorithm of voltage quantities to form a joint location criterion, which improves the accuracy of fault location. Finally, for the single-ended ranging method, which is easily affected by the wave impedance discontinuity points in the system or the transition resistance in the line, this article introduces a fault ranging algorithm based on double-ended electrical quantities, which improves the accuracy and applicable range of fault ranging. Through simulation verification, we found that the matrix algorithm based on the electrical quantity can accurately locate the fault section in the case of a single fault with a single power supply. The proposed joint matrix algorithm can accurately locate the fault section in the case of a single fault with multiple power sources. The ranging algorithm based on double-ended electrical quantities has higher ranging accuracy in both interphase short circuits and grounded short circuits, and the ranging results are not affected by the fault type, fault location and transition resistance, which can effectively improve the efficiency and reliability of fault location. Full article

This study focuses on the Xiaoxing’an Mountains, examining the evolution of ecological security patterns and suggesting optimization strategies by integrating carbon storage and landscape connectivity, using multi-source data from 2000, 2010, and 2020. The study provides a comprehensive assessment of the region’s ecological security by estimating carbon stocks using the InVEST model, analyzing landscape connectivity through MSPA, and spatially extracting ecological corridors and nodes using circuit theory. The key findings are as follows: (1) High-value areas for carbon storage and landscape connectivity are primarily concentrated in the southeastern and northwestern forested mountain regions; (2) Ecological source areas are predominantly concentrated in the southeast and dispersed in the north, with the total area peaking in 2010 at 47,054.10 km²; (3) Northern ecological corridors are dense, radiating in a spider-web pattern, with pinch points concentrated at the corridor termini; southeastern corridors are sparse, mainly short, with fewer pinch points; (4) The area of ecological barriers increased by 280% over the past 20 years. Four major barrier zones were identified, all located at the junction of forest and farmland in the northwest, primarily composed of wetlands, drylands, and rural residential areas; (5) Based on the evolutionary characteristics of the Ecological Security Pattern over the past 20 years, an “axis, two belts, four zones, and multiple cores” ecological security planning framework was proposed, along with corresponding strategies. This study provides theoretical support and practical guidance for enhancing regional ecological network stability, optimizing landscape connectivity, and strengthening carbon sink functions. Full article

The Jiangnan Orogen (South China) hosts abundant gold deposits, but the absence of accurate constraints on the ore-forming age and process has resulted in significant controversy regarding their origins. The Yangwantuan gold deposit, located in the central part of the Jiangnan Orogen, is characterized by multi-stage quartz veins linked to mineralization and alteration. The mineralization can be divided into three stages, namely the barren quartz–sericite stage (I); the quartz–sericite–native gold–polymetallic sulfide stage (II, including the quartz–sericite–dolomite–native gold–polymetallic sulfide (IIA) and quartz–chlorite–sericite–native gold–arsenopyrite (IIB) substages); and the quartz–dolomite–calcite–arsenopyrite (III) stage. On the basis of the mineralization and alteration sequence and quartz’s internal texture, 11 generations of quartz are determined, including gray QzIa and dark QzIb in Stage I; oscillatory-zoning QzIIa, homogeneous QzIIb, and veined QzIIc in Stage IIA; homogeneous QzIId, QzIIe trapping sulfide inclusions, and veined QzIIf in Stage IIB; and gray QzIIIa, dark QzIIIb, and veined QzIIIc in Stage III. The decrease in Al content corresponds to an increase in pH from QzIa to QzIb, favoring the transportation of gold in the fluid. The sharp drop in temperature and the increment of pH, revealed by Al and Ti content variations from QzIIa to QzIIb, indicates a strong water–rock interaction, consistent with the occurrence of arsenopyrite in the wall rock. Therefore, the gold precipitation in Stage IIA may be triggered by the consumption of H₂S through water–rock interaction, whereas during Stage IIB and III_, the precipitation of gold is attributed to the consumption of H₂S as a result of the formation of abundant sulfide, which is supported by the coexistence of sulfide and QzIIf and QzIIIc. The Stage IIA sericite Rb-Sr isochron age of 397 ± 11 Ma (MSWD = 0.8, n = 32) suggests that the mineralization age is closely related to the Devonian Orogeny. The absence of contemporaneous magmatic rock and quartz Al and Ti concentrations both indicate that the Yangwantuan deposit may be classified as an orogenic gold deposit. Full article

Mainstream strategies for protecting wealth from inflation involve diversification into traditional assets like common stocks, gold, fixed-income securities, and real estate. However, a significant contributor to inflation has been the rising energy prices, which have been the main underlying cause of several past recessions and high inflation periods. Investments in distributed generation with solar photovoltaics (PV) present a promising opportunity to hedge against inflation, considering non-taxed profits from PV energy generation. To investigate that potential, this study quantifies the return on investment (ROI), internal rate of return (IRR), payback period, net present cost, and levelized cost of energy of PV by running Solar Alone Multi-Objective Advisor (SAMA) simulations on grid-connected PV systems across different regions with varying inflation scenarios. The case studies are San Diego, California; Boston, Massachusetts; Santiago, Chile; and Buenos Aires, Argentina. Historical inflation data are also imposed on San Diego to assess PV system potential in dynamic inflammatory conditions, while Boston and Santiago additionally analyze hybrid PV-battery systems to understand battery impacts under increasing inflation rates. Net metering credits vary by location. The results showed that PV could be used as an effective inflation hedge in any region where PV started economically and provided increasingly attractive returns as inflation increased, particularly when taxes were considered. The varying values of the ROI and IRR underscore the importance of region-specific financial planning and the need to consider inflation when evaluating the long-term viability of PV systems. Finally, more capital-intensive PV systems with battery storage can become profitable in an inflationary economy. Full article

The decarbonization of the electricity system coupled with the electrification of transport, heat, and industry represents a practical and cost-effective approach to deep decarbonization. A key question is as follows: where to build new solar and wind farms? This study presents a cost-based approach to evaluate land parcels for solar and wind farm suitability using colour-coded heatmaps that visually depict favourable locations. An indicative cost of electricity is calculated and classified for each pixel by focusing on key factors including the resource availability, proximity to transmission infrastructure and load centres, and exclusion of sensitive areas. The proposed approach mitigates the subjectivity associated with traditional multi-criteria decision-making methods, in which both the selection of siting factors and the assignment of their associated weightings rely highly on the subjective judgements of experts. The methodology is applied to Australia, South Korea, and Indonesia, and the results show that proximity to high-voltage transmission and load centres is a key factor affecting site selection in Australia and Indonesia, while connection costs are less critical in South Korea due to its smaller land area and extensive infrastructure. The outcomes of this study, including heatmaps and detailed statistics, are made publicly available to provide both qualitative and quantitative information that allows comparisons between regions and within a region. This study aims to empower policymakers, developers, communities, and individual landholders to make informed decisions and, ultimately, to facilitate strategic renewable energy deployment and contribute to global decarbonization. Full article